1) The study analyzed how age, gender, personality traits, and language use on Twitter can predict and provide insights into mental illnesses like depression and PTSD. 2) The results showed that mentally ill users tended to be higher in neuroticism, more introverted, and less agreeable, and that controlling for age and gender was important. 3) Language models that incorporated a large number of features including personality, affect, topics, and n-grams were most accurate at detecting mental illnesses, demonstrating the importance of personality in understanding conditions like depression.

1. The Role of Personality, Age and Gender
in Tweeting about Mental Illnesses
Daniel Preoţiuc-Pietro, Johannes Eichstaedt, Gregory Park, Maarten Sap
Laura Smith, Victoria Tobolsky, H. Andrew Schwartz and Lyle Ungar

2. Problem
● Mental illnesses are underdiagnosed

3. Problem
● Mental illnesses are underdiagnosed
This Study:
● Explore the predictive power of demographic and
personality based features.
● Find insights provided by each feature.

4. Data
● Twitter self-reports
‘I have been diagnosed with depression’
● depression: 483
● PTSD: 370
● controls: 1104
● each user has avg. 3400 messages
(Coppersmith et. al, CLPsych 2014)

5. Study Setup
Twitter language
mental
illness
classification

6. Study Setup
Twitter language
age,
gender,
personality
?
mental
illness
classification

7. Age, Gender
● Model from FB and Twitter data
(Sap et. al, EMNLP 2014)

8. Age, Gender
● Model from FB and Twitter data
(Sap et. al, EMNLP 2014)

9. Age, Gender

10. Personality
● Big 5 Personality Traits
○ openness
○ conscientiousness
○ extraversion
○ agreeableness
○ neuroticism
● Model from Facebook data
(Park et. al 2014)

11. Personality
● Big 5 Personality Traits
○ openness
○ conscientiousness
○ extraversion
○ agreeableness
○ neuroticism
● Model from Facebook data
(Park et. al 2014)

12. Personality
● mentally ill users:
1. high on neuroticism
2. more introverted
3. less agreeable

13. Personality
● mentally ill users:
1. high on neuroticism
2. more introverted
3. less agreeable
● controlling for
age and gender

14. Personality

15. Age, Gender, Personality

16. Affect and Intensity ● Model trained on 3000
annotated FB posts and
applied to all user posts
(to be published)
● circumplex model similar to
valence & arousal (ANEW)

17. Affect and Intensity ● Model trained on 3000
annotated FB posts and
applied to all user posts
(to be published)
● circumplex model similar to
valence & arousal (ANEW)

18. Affect and Intensity
● mentally ill users are less
aroused and less positive

19. LIWC
● standard psychologically inspired dictionaries
● 64 categories such as:
parts-of-speech
topical categories
emotions
● standard baseline for open vocabulary approaches

20. LIWC

21. LIWC
7 features 64 features

22. Topics
● posteriors computed using
Latent Dirichlet Allocation (LDA)
● underlying set of Facebook statues
(same data as personality model)
● 2000 topics in total

23. Topics

24. Topics 7 features 64 features 2000 features

25. Topics: Depression
Topics controlled for age and gender

26. Topics: PTSD
Topics controlled for age and gender

27. Topics:
PTSD,
Depression, &
Neuroticism

28. + Dep, +++ PTSD
++ Dep, ++ PTSD
+++ Dep, 0 PTSD

29. + Dep, +++ PTSD
++ Dep, ++ PTSD
+++ Dep, 0 PTSD

30. + Dep, +++ PTSD
++ Dep, ++ PTSD
+++ Dep, 0 PTSD

31. Topics

32. 1-3 grams

33. 1-3 grams 7 64 2k ~25k

34. 1-3 grams: Depressed vs. Controls

35. 1-3 grams: PTSD vs. Controls

36. 1-3 grams: Depressed vs. PTSD
Almost nothing left when controlling for age and gender

37. Other features…
● use metadata features
# friends, #statuses
● use different word clusters
Brown clustering, NPMI Spectral clustering, Word2Vec/GloVe embeddings
● linear ensemble of logistic regression
classifiers
Mental Illness detection at the World Well-Being Project for the CLPsych 2015 Shared Task
D. Preotiuc-Pietro, M. Sap, H.A. Schwartz, L. Ungar

38. ROC Curve
Depressed
vs.
Controls

39. ROC Curve
PTSD
vs.
Controls

40. ROC Curve
Depressed
vs.
PTSD

41. Take Home
● Control the analysis for age & gender

42. Take Home
● Control the analysis for age & gender
● Personality plays an important role in mental illnesses
(depression auc: 7 features -> .78; 25k features-> .86)

43. Take Home
● Control the analysis for age & gender
● Personality plays an important role in mental illnesses
(depression auc: 7 features -> .78; 25k features-> .86)
● Language use of depressed/PTSD reveals symptoms,
emotions, and cognitive processes.

44. Thank you!
wwbp.org lexhub.org